Downstream Model Design of Pre-trained Language Model for Relation Extraction Task

• URL: http://arxiv.org/abs/2004.03786v1
• Date: Wed, 8 Apr 2020 03:16:06 GMT
• Title: Downstream Model Design of Pre-trained Language Model for Relation Extraction Task
• Authors: Cheng Li, Ye Tian
• Abstract summary: Supervised relation extraction methods based on deep neural network play an important role in the recent information extraction field. New network architecture with a special loss function is designed to serve as a downstream model of PLMs for supervised relation extraction.
• Score: 6.608858001497843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Supervised relation extraction methods based on deep neural network play an important role in the recent information extraction field. However, at present, their performance still fails to reach a good level due to the existence of complicated relations. On the other hand, recently proposed pre-trained language models (PLMs) have achieved great success in multiple tasks of natural language processing through fine-tuning when combined with the model of downstream tasks. However, original standard tasks of PLM do not include the relation extraction task yet. We believe that PLMs can also be used to solve the relation extraction problem, but it is necessary to establish a specially designed downstream task model or even loss function for dealing with complicated relations. In this paper, a new network architecture with a special loss function is designed to serve as a downstream model of PLMs for supervised relation extraction. Experiments have shown that our method significantly exceeded the current optimal baseline models across multiple public datasets of relation extraction.

Related papers

• Task-Distributionally Robust Data-Free Meta-Learning [99.56612787882334]
  Data-Free Meta-Learning (DFML) aims to efficiently learn new tasks by leveraging multiple pre-trained models without requiring their original training data. For the first time, we reveal two major challenges hindering their practical deployments: Task-Distribution Shift ( TDS) and Task-Distribution Corruption (TDC)
  arXiv  Detail & Related papers  (2023-11-23T15:46:54Z)
• PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming [30.597623178206874]
  We propose PromptRE, a novel weakly-supervised document-level relation extraction method. PromptRE incorporates the label distribution and entity types as prior knowledge to improve the performance. Experimental results on ReDocRED, a benchmark dataset for document-level relation extraction, demonstrate the superiority of PromptRE over baseline approaches.
  arXiv  Detail & Related papers  (2023-10-13T17:23:17Z)
• AdaMerging: Adaptive Model Merging for Multi-Task Learning [68.75885518081357]
  This paper introduces an innovative technique called Adaptive Model Merging (AdaMerging) It aims to autonomously learn the coefficients for model merging, either in a task-wise or layer-wise manner, without relying on the original training data. Compared to the current state-of-the-art task arithmetic merging scheme, AdaMerging showcases a remarkable 11% improvement in performance.
  arXiv  Detail & Related papers  (2023-10-04T04:26:33Z)
• Curriculum Modeling the Dependence among Targets with Multi-task Learning for Financial Marketing [26.80709680959278]
  We propose a prior information merged model (textbfPIMM) for multiple sequential dependence task learning. The PIM randomly selects the true label information or the prior task prediction with a soft sampling strategy to transfer to the downstream task during the training. The offline experimental results on both public and product datasets verify that PIMM outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-04-25T07:55:16Z)
• A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning [11.076005074172516]
  reinforcement learning algorithms can easily encounter catastrophic forgetting or interference when faced with lifelong streaming information. We propose a scalable lifelong RL method that dynamically expands the network capacity to accommodate new knowledge. We show that our method successfully facilitates scalable lifelong RL and outperforms relevant existing methods.
  arXiv  Detail & Related papers  (2022-05-22T09:48:41Z)
• Improving Long Tailed Document-Level Relation Extraction via Easy Relation Augmentation and Contrastive Learning [66.83982926437547]
  We argue that mitigating the long-tailed distribution problem is crucial for DocRE in the real-world scenario. Motivated by the long-tailed distribution problem, we propose an Easy Relation Augmentation(ERA) method for improving DocRE.
  arXiv  Detail & Related papers  (2022-05-21T06:15:11Z)
• Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning [65.268245109828]
  In data-rich domains such as vision, language, and speech, deep learning prevails to deliver high-performance task-specific models. Deep learning in resource-limited domains still faces multiple challenges including (i) limited data, (ii) constrained model development cost, and (iii) lack of adequate pre-trained models for effective finetuning. Model reprogramming enables resource-efficient cross-domain machine learning by repurposing a well-developed pre-trained model from a source domain to solve tasks in a target domain without model finetuning.
  arXiv  Detail & Related papers  (2022-02-22T02:33:54Z)
• Temporal Relation Extraction with a Graph-Based Deep Biaffine Attention Model [0.0]
  We propose a novel temporal information extraction model based on deep biaffine attention. We experimentally demonstrate that our model achieves state-of-the-art performance in temporal relation extraction.
  arXiv  Detail & Related papers  (2022-01-16T19:40:08Z)
• SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction [51.27558374091491]
  We propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for relation extraction. Based on a broad spectrum of carefully designed tasks, our proposed SAIS method not only extracts relations of better quality due to more effective supervision, but also retrieves the corresponding supporting evidence more accurately.
  arXiv  Detail & Related papers  (2021-09-24T17:37:35Z)
• Exploring Task Difficulty for Few-Shot Relation Extraction [22.585574542329677]
  Few-shot relation extraction (FSRE) focuses on recognizing novel relations by learning with merely a handful of annotated instances. We introduce a novel approach based on contrastive learning that learns better representations by exploiting relation label information.
  arXiv  Detail & Related papers  (2021-09-12T09:40:33Z)
• Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction [166.87111665908333]
  We propose a novel multi-task learning method called Task-Feature Collaborative Learning (TFCL) Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks.
  arXiv  Detail & Related papers  (2020-04-29T02:32:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.